Parametric imaging of dynamic long-axial-field-of-view PET scans: Technical challenges, statistical insights and clinical applications

Dynamic long-axial-field-of-view (LAFOV) PET imaging offers unprecedented opportunities for quantitative assessment of tracer kinetics across the entire body. This review discusses the core technical challenges posed by LAFOV datasets in parametric image generation and introduces methodological developments from a statistical perspective, including arterial input function strategies, classical and flexible kinetic models (compartment models, spectral analysis, adiabatic approximation to the tissue homogeneity, and non-parametric models), graphical techniques (Patlak, Logan, and their variants), and emerging directions such as direct parametric reconstruction, dimension reduction, and deep learning. These methodologies are further linked to dynamic clinical protocols designed to shorten scanning times, enable multi-tracer injections, support low-dose imaging, and open avenues for novel tracer and drug development. Finally, we summarize the most recent software packages, particularly those tailored for LAFOV PET parametric imaging. These advances indicate that reliable parametric imaging holds promise for broader clinical adoption, grounded in robust modeling, multi-center validation, and ongoing software advancements.